Background and Aims: The sinus anatomy should be well‐understood by the sinus surgeons to carry out functional endoscopic sinus surgery carefully. That's why CT scans are vital to provide essential clarity and accuracy for comprehensive presurgical planning with minimal risks. The aim of this study was to evaluate the frequency of anatomic variations of the paranasal sinus region by using multidetector computed tomography.Methods: A cross‐sectional study of one hundred and fifty‐three patients of all age groups was carried out in the radiology department of Shalamar Hospital Lahore from 20 January, 2024 to 10 April, 2024 to evaluate anatomical variations presented to the facility by using multidetector CT. Data were collected using a standardized data collection sheet and analyzed using SPSS version 25.0.Results: The study included 94 males and 59 females with a median age of 43.9 years. Among the study population, 4.6% had aplasia of the frontal sinus, 84.3% had bilateral frontal sinus, but 13.7% had unilateral, 42.5% had more than 2 chambers of frontal sinus, and 9.8% had hypoplasia with persistent metopic suture. Four patterns of sphenoid sinus pneumatization were recognized: the conchal, the presellar, the sellar, and the post‐sellar, with prevalence rates of 2.0%, 11.1%, 28.1%, and 56.9%, respectively. 0.7% had hypoplasia of maxillary sinus, 9.2% had exostoses, 13.7% had extension of teeth roots to maxillary sinus, and 3.3% had maxillary sinus septations. Ethmoidal bulla, Agger nasi cells, and Haller cells had frequencies of 49%, 32.7%, and 30.7%, respectively.Conclusions: The most common anatomical variations are bilateral frontal sinus, post‐sellar pneumatization of sphenoid sinus, ethmoidal bulla in ethmoid sinus, and extension of teeth roots to maxillary sinus. Such characteristics and findings are to be evaluated for the management of pathologies associated with these variations and consequent surgical interventions.

Background/Objectives: Alzheimer’s disease is the leading cause of dementia, marked by progressive cognitive decline and a severe socioeconomic burden. Early and accurate diagnosis is crucial to enhancing patient outcomes, yet traditional clinical and imaging assessments are often limited in sensitivity, particularly at early stages. This study presents a dual-modal framework that integrates symptom-based clinical data with magnetic resonance imaging (MRI) using machine learning (ML) and deep learning (DL) models, enhanced by explainable AI (XAI). Methods: Four ML classifiers—K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF)—were trained on demographic and clinical features. For stage-wise classification, five DL models—CNN, EfficientNetB3, DenseNet-121, ResNet-50, and MobileNetV2—were applied to MRI scans. Interpretability was incorporated through SHAP and Grad-CAM visualizations. Results: Random Forest achieves the highest accuracy of 97% on clinical data, while CNN achieves the best overall performance of 94% in MRI-based staging. SHAP and Grad-CAM were used to find clinically relevant characteristics and brain areas, including hippocampal atrophy and ventricular enlargement. Conclusions: Integrating clinical and imaging data and interpretable AI improves the accuracy and reliability of AD staging. The proposed model offers a valid and clear diagnostic route, which can assist clinicians in making timely diagnoses and adjusting individual treatment.

Background: Acute diarrhea (AD), which is defined as frequent passing of liquid stools compared to normal, is a serious and worrying problem and remains a concern for healthcare systems because of its high mortality cause in children under 5 years old. The authors’ study aimed to present the prevalence and to describe the clinical manifestation of AD among under-5 children. Methods: From June 2022 to May 2023, the authors conducted a retrospective, descriptive and cross-sectional study including all patients aged 0–5 years hospitalized for AD. Results: Out of 512 patients, only 197 (38.5%) children with AD were selected for our study. The average age is 25.5 months, and the sex ratio is 1.11. Some families (75.1%) have clean latrines, and 21.8% use water from the river. Inaccessibility to clean water and intolerance or food poisoning were the causes of acute diarrhea in children. Major signs and symptoms are fever, dehydration and vomiting. Weight loss and malnutrition are the major complications of AD in children. The treatment of AD is provided by oral rehydration solutions and antibiotics. Conclusion: The study highlights the significant prevalence of acute diarrhea among under-5 children underscores the importance of preventive measures and government intervention, such as the introduction of rotavirus vaccination. However, conclusions regarding prevalence rates should be interpreted with caution due to the lack of detailed population data.

Ebola virus disease (EVD) has long been a major public health concern for Democratic Republic of the Congo (DR Congo). First identified in DR Congo in 1976, the country has witnessed more than 25 outbreaks of this deadly disease, which has a case fatality rate of nearly 90% and manifesting with symptoms such as diarrhoea, vomiting, stomachache and haemorrhagic fever. African fruit bats have been speculated to be the reservoir of this virus. DR Congo is currently facing another EVD outbreak simultaneously with other communicable diseases, rendering it vulnerable to a shortage of medical and paramedical staff along with distrust among remote communities towards local authorities due to armed conflict and political instability. Moreover, lack of ring vaccinations and inefficient surveillance of suspected individuals are some other significant hurdles in disease control. Despite the availability of rVSV-ZEBOV/Erbevo vaccine and many antibody-based vaccines, challenges including politicization, low access to remote communities, and illiteracy have limited their effectiveness. Recently, the Congolese govt. has put in efforts such as building local capacities at the health zone level, outbreak control intervention, community engagement and social mobilization to counter the rising EVD cases. Four successive Strategic Response Plans have been implemented to increase resource mobilization by DR Congo and her partners. The Spread of zoonotics such as EVD can be confronted by implementing the One Health approach, which involves medical staff, veterinarians and public health officials.

Background/Objective: Multimodal image registration plays a critical role in modern medical imaging, enabling the integration of complementary modalities such as positron emission tomography (PET) and computed tomography (CT). This study compares the performance of three widely used image registration techniques—Demons Image Registration with Modality Transformation, Free-Form Deformation using the Medical Image Registration Toolbox (MIRT), and MATLAB Intensity-Based Registration—in terms of improving PET/CT image alignment. Methods: A total of 100 matched PET/CT image slices from a clinical scanner were analysed. Preprocessing techniques, including histogram equalisation and contrast enhancement (via imadjust and adapthisteq), were applied to minimise intensity discrepancies. Each registration method was evaluated under varying parameter conditions with regard to sigma fluid (range 4–8), histogram bins (100 to 256), and interpolation methods (linear and cubic). Performance was assessed using quantitative metrics: root mean square error (RMSE), mean squared error (MSE), mean absolute error (MAE), the Pearson correlation coefficient (PCC), and standard deviation (STD). Results: Demons registration achieved optimal performance at a sigma fluid value of 6, with an RMSE of 0.1529, and demonstrated superior computational efficiency. The MIRT showed better adaptability to complex anatomical deformations, with an RMSE of 0.1725. MATLAB Intensity-Based Registration, when combined with contrast enhancement, yielded the highest accuracy (RMSE = 0.1317 at alpha = 6). Preprocessing improved registration accuracy, reducing the RMSE by up to 16%. Conclusions: Each registration technique has distinct advantages: the Demons algorithm is ideal for time-sensitive tasks, the MIRT is suited to precision-driven applications, and MATLAB-based methods offer flexible processing for large datasets. This study provides a foundational framework for optimising PET/CT image registration in both research and clinical environments.

Background and Aim: Due to the global shortage in the surgical workforce, especially in low‐resource settings, one solution to increase surgical volume is to delegate certain roles of surgeons to other trained non‐surgeon health workers. However, quantifying the costs and benefits of surgical task‐shifting has several challenges associated with it. The purpose of this study was to conduct a critical appraisal of studies on the cost‐effectiveness of task shifting in surgical care. Methods: A systematic review was done using searches on four major electronic bibliographic databases (EMBASE, Ovid MEDLINE(R), Web of Science, Econlit) up to June 2021. Studies were selected based on pre‐defined inclusion criteria and relevant data were extracted. Results: A total of 16 studies were eligible for inclusion in the review. 14 of them were done in low‐ and middle‐income countries while the other two, in high income countries. Findings showed that task shifting to non‐surgeons lowers the total cost of surgery and increases coverage without any significant difference in outcome when compared with surgeons. Conclusion: Task shifting in surgical care is considered to be cost‐effective, improving the efficiency and access to surgical care in both low‐ and high‐income countries. Methodological challenges make study findings difficult to generalize. The costs and outcome values are dependent on the choice of comparator, hospital setting, cost items, and surgical procedure included. However, there is a need for more published data in different locations to support evidence for policymaking.